Using Local Integral Invariants for Object Recognition in Complex Scenes

Abstract

This paper investigates the use of local descriptors that are based on integral invariants for the purpose of object recognition in cluttered scenes. Integral invariants capture the local structure of the neighborhood around the points where they are computed. This makes them very well suited for constructing highly-discriminative local descriptors. The features are by definition invariant to Euclidean motion. We show how to extend the local features to be scale invariant. Regarding the robustness to intensity changes, two types of kernels used for extracting the feature vectors are investigated. The effect of the feature vector dimensionality and the performance in the presence of noise are also examined. Promising results are obtained using a dataset that contains instances of objects that are viewed in difficult situations that include clutter and occlusion.